The Engineers' Bookshelf

Our perception of an object’s size arises from the integration of multiple sources of visual information including retinal size, perceived distance and its size relative to other objects in the visual field. This constructive process is revealed through a number of classic size illusions such as the Delboeuf Illusion, the Ebbinghaus Illusion and others illustrating size constancy. Here we present a novel variant of the Delbouef and Ebbinghaus size illusions that we have named the Binding Ring Illusion. The illusion is such that the perceived size of a circular array of elements is underestimated when superimposed by a circular contour – a binding ring – and overestimated when the binding ring slightly exceeds the overall size of the array. Here we characterize the stimulus conditions that lead to the illusion, and the perceptual principles that underlie it. Our findings indicate that the perceived size of an array is susceptible to the assimilation of an explicitly defined superimposed contour. Our results also indicate that the assimilation process takes place at a relatively high level in the visual processing stream, after different spatial frequencies have been integrated and global shape has been constructed. We hypothesize that the Binding Ring Illusion arises due to the fact that the size of an array of elements is not explicitly defined and therefore can be influenced (through a process of assimilation) by the presence of a superimposed object that does have an explicit size

Understanding Misperceived Size through Assimilation in a Novel Illusion: The Binding Ring

How do we perceive the size of objects? This research attempts answer a small part of that question by describing and quantifying the binding ring illusion, a novel (and previously uncharacterized) illusion of misperceived size. Following the establishment of the binding ring as a legitimate size illusion, experiments were performed in an attempt to identify what visual processing stream mechanisms were responsible for the illusion and further, to obtain any information as to the relative whereabouts of said mechanism(s) in the visual processing stream. These experiments were performed because many processing mechanisms of the visual stream (and their relative location therein), especially those in the relatively ‘high’ portion of the visual stream, are very poorly understood. The results of the study gave three primary results: first, the binding ring illusion occurs primarily through a process of assimilation; second, this assimilation process is heavily influenced by a process of perceptual unification; and third, the mechanisms of assimilation and unification are likely located relatively high in the visual processing stream, very likely occurring after spatial frequency integration and either after or during the creation of global shape representations

SS18 Together with Animal-Specific Factors Defines Human BAF-Type SWI/SNF Complexes

Contains fulltext : 94049.pdf (publisher's version ) (Open Access

The Quality of Response Time Data Inference: A Blinded, Collaborative Assessment of the Validity of Cognitive Models

Most data analyses rely on models. To complement statistical models, psychologists have developed cognitive models, which translate observed variables into psychologically interesting constructs. Response time models, in particular, assume that response time and accuracy are the observed expression of latent variables including 1) ease of processing, 2) response caution, 3) response bias, and 4) non-decision time. Inferences about these psychological factors, hinge upon the validity of the models’ parameters. Here, we use a blinded, collaborative approach to assess the validity of such model-based inferences. Seventeen teams of researchers analyzed the same 14 data sets. In each of these two-condition data sets, we manipulated properties of participants’ behavior in a two-alternative forced choice task. The contributing teams were blind to the manipulations, and had to infer what aspect of behavior was changed using their method of choice. The contributors chose to employ a variety of models, estimation methods, and inference procedures. Our results show that, although conclusions were similar across different methods, these "modeler’s degrees of freedom" did affect their inferences. Interestingly, many of the simpler approaches yielded as robust and accurate inferences as the more complex methods. We recommend that, in general, cognitive models become a typical analysis tool for response time data. In particular, we argue that the simpler models and procedures are sufficient for standard experimental designs. We finish by outlining situations in which more complicated models and methods may be necessary, and discuss potential pitfalls when interpreting the output from response time models

Applications of Hierarchical Bayesian Cognitive Modeling

The hierarchical Bayesian approach to cognitive modeling often provides a quality of inference that cannot be matched with other analytical methods. In addition, the general approach is quite flexible, and can be utilized to great effect in many analytical settings. I illustrate these qualities in two applications of hierarchical Bayesian cognitive models. In the first application, I revisit a transfer-of-training study. First, I discuss a hierarchical cognitive model that describes the transfer-of-training data. I then illustrate how that hierarchical cognitive model can be further extended in order to create a cognitive latent variable model. Critically, this cognitive latent variable model directly models the latent effects of training and transfer on the cognitive parameters that drive participant behavior. I then provide an in depth analysis to illustrate how this cognitive latent variable model provides a quality of inference that far surpasses more standard analytical approaches. In the second application, I perform a cognitive meta-analysis on the spatial congruency bias literature. To do this, I extend the hierarchical Bayesian cognitive model into an integrative data analysis, creating a Model-based Integrative Data AnalysiS (MIDAS). Using this model, I create a model that is capable of simultaneously estimating cognitive effects at the individual, within-experiment, and between-experiment levels, which is what allows us to estimate cognitive effects in a meta-analytical setting